04 - R framework with IMPACT - session 4

Author

Yann Say

Published

April 10, 2024

library(analysistools)
library(dplyr)

my_data <- analysistools::analysistools_MSNA_template_data

sampling_frame <- data.frame(
  strata = c("admin1a", "admin1b", "admin1c"),
  population = c(100000, 200000, 300000)
)

set.seed(1323)
my_data <- my_data |>
  mutate(num_aged_school_children = round(runif(100, min = 0, max = 5)),
         num_enrolled_school_children = round(runif(100, min = 0, max = 5)),
         num_enrolled_school_children = case_when(num_aged_school_children == 0 ~ NA, num_aged_school_children < num_enrolled_school_children ~ num_aged_school_children,
                                                  TRUE ~ num_enrolled_school_children
                                                  ))

Analysis - extended analysis

The framework is built around 4 steps: cleaning, composition, analysis, outputs

Cleaning: Any manipulation to go from the raw data to the clean data
Composition: Any manipulation before the analysis e.g. adding indicators, adding information from loop, main dataset, or any other dataset (e.g. previous round), aok aggregation, etc.
Analysis: Any manipulation regarding only the analysis.
Outputs: Any manipulation to format the outputs. Outputs are created from the results table, from the stat + analysis key

The following section will present some introduction about the analysis.

The third step of the framework is the analysis. The analysis step aims to create a long table with one result per line and an analysis key. That table is not made for a human to read it but to store some information.

The analysis key format is currently :

analysis type @/@ analysis variable %/% analysis variable value @/@ grouping variable %/% grouping variable value
analysis type @/@ dependent variable %/% dependent variable value @/@ independent variable %/% independent variable value

If there are two or more grouping variables it would look like that

analysis type @/@ analysis variable %/% analysis variable value @/@ grouping variable 1 %/% grouping variable value 1 -/- grouping variable 2 %/% grouping variable value 2

Same would apply for analysis variable in case of a ratio.

The current analysis types are :

mean
median
prop_select_one: proportion for select one
prop_select_multiple: proportion for select multiple
ratio

create_*

create_* functions will create, transform something, e.g. creating a cleaning log with the checks to be filled, create analysis results table, create an output.

Outputs from create_* functions outputs can be in different shape, format, etc.

create_* function is catch-all.

`create_analysis` and list of analysis (loa)

List of analysis (loa) is a list with all analysis to be performed. It takes the form of a data frame with minimum 4 columns:

analysis_type : The analysis type that should be performed.
analysis_var : The analysis variable or dependent variable.
group_var : The grouping variable or independent variable.
level : The confidence level (expressed between 0 and 1).

my_loa <- analysistools::analysistools_MSNA_template_loa

my_loa

analysis_type	analysis_var	group_var	level
prop_select_one	admin1	NA	0.95
mean	income_v1_salaried_work	NA	0.95
median	income_v1_salaried_work	NA	0.95
mean	expenditure_debt	NA	0.95
median	expenditure_debt	NA	0.95
prop_select_one	wash_drinkingwatersource	NA	0.95
prop_select_multiple	edu_learning_conditions_reasons_v1	NA	0.95
mean	income_v1_salaried_work	admin1	0.95
median	income_v1_salaried_work	admin1	0.95
mean	expenditure_debt	admin1	0.95
median	expenditure_debt	admin1	0.95
prop_select_one	wash_drinkingwatersource	admin1	0.95
prop_select_multiple	edu_learning_conditions_reasons_v1	admin1	0.95

The loa can be passed as argument to the create_analysis function.

my_data <- my_data %>% 
  add_weights(sampling_frame, "admin1", "strata", "population")

my_design <- srvyr::as_survey_design(my_data, weights = "weights", strata = "admin1")
my_results <- create_analysis(my_design, loa = my_loa, sm_separator = "/")

head_results_table <- my_results$results_table %>% 
  head(5)

tail_results_table <- my_results$results_table %>% 
  tail(5)

rbind(head_results_table,tail_results_table)

analysis_type	analysis_var	analysis_var_value	group_var	group_var_value	stat	stat_low	stat_upp	n	n_total	n_w	n_w_total	analysis_key
prop_select_one	admin1	admin1a	NA	NA	0.1666667	0.1666667	0.1666667	31	100	16.66667	100	prop_select_one @/@ admin1 %/% admin1a @/@ NA %/% NA
prop_select_one	admin1	admin1b	NA	NA	0.3333333	0.3333333	0.3333333	27	100	33.33333	100	prop_select_one @/@ admin1 %/% admin1b @/@ NA %/% NA
prop_select_one	admin1	admin1c	NA	NA	0.5000000	0.5000000	0.5000000	42	100	50.00000	100	prop_select_one @/@ admin1 %/% admin1c @/@ NA %/% NA
mean	income_v1_salaried_work	NA	NA	NA	20.0472777	19.6448025	20.4497529	100	100	100.00000	100	mean @/@ income_v1_salaried_work %/% NA @/@ NA %/% NA
median	income_v1_salaried_work	NA	NA	NA	20.0000000	20.0000000	21.0000000	100	100	100.00000	100	median @/@ income_v1_salaried_work %/% NA @/@ NA %/% NA
prop_select_multiple	edu_learning_conditions_reasons_v1	unreliable_technology	admin1	admin1c	0.5000000	0.3450192	0.6549808	21	42	25.00000	50	prop_select_multiple @/@ edu_learning_conditions_reasons_v1 %/% unreliable_technology @/@ admin1 %/% admin1c
prop_select_multiple	edu_learning_conditions_reasons_v1	lack_equipment	admin1	admin1c	0.5714286	0.4180373	0.7248198	24	42	28.57143	50	prop_select_multiple @/@ edu_learning_conditions_reasons_v1 %/% lack_equipment @/@ admin1 %/% admin1c
prop_select_multiple	edu_learning_conditions_reasons_v1	other	admin1	admin1c	0.4047619	0.2526185	0.5569053	17	42	20.23810	50	prop_select_multiple @/@ edu_learning_conditions_reasons_v1 %/% other @/@ admin1 %/% admin1c
prop_select_multiple	edu_learning_conditions_reasons_v1	dont_know	admin1	admin1c	0.5000000	0.3450192	0.6549808	21	42	25.00000	50	prop_select_multiple @/@ edu_learning_conditions_reasons_v1 %/% dont_know @/@ admin1 %/% admin1c
prop_select_multiple	edu_learning_conditions_reasons_v1	prefer_not_to_answer	admin1	admin1c	0.4285714	0.2751802	0.5819627	18	42	21.42857	50	prop_select_multiple @/@ edu_learning_conditions_reasons_v1 %/% prefer_not_to_answer @/@ admin1 %/% admin1c

`create_analysis_ratio`

To calculate ratio, there are two functions create_analysis_ratio or create_analysis with a loa with more information.

my_loa_with_ratio <- read.csv("inputs/07 - example - loa_with_ratio.csv")

my_loa_with_ratio %>% 
  filter(analysis_type == "ratio") |>
  select(analysis_type, analysis_var, group_var, analysis_var_numerator, analysis_var_denominator)

analysis_type	analysis_var	group_var	analysis_var_numerator	analysis_var_denominator
ratio	NA	NA	num_enrolled_school_children	num_aged_school_children
ratio	NA	admin1	num_enrolled_school_children	num_aged_school_children

my_results_with_ratio <- create_analysis(my_design, loa = my_loa_with_ratio, sm_separator = "/")

my_results_with_ratio$results_table %>% 
  filter(analysis_type == "ratio")

analysis_type	analysis_var	analysis_var_value	group_var	group_var_value	stat	stat_low	stat_upp	n	n_total	n_w	n_w_total	analysis_key
ratio	num_enrolled_school_children %/% num_aged_school_children	NA %/% NA	NA	NA	0.6874563	0.6054471	0.7694654	89	89	88.12226	88.12226	ratio @/@ num_enrolled_school_children %/% NA -/- num_aged_school_children %/% NA @/@ NA %/% NA
ratio	num_enrolled_school_children %/% num_aged_school_children	NA %/% NA	admin1	admin1a	0.6881720	0.5646192	0.8117249	29	29	15.59140	15.59140	ratio @/@ num_enrolled_school_children %/% NA -/- num_aged_school_children %/% NA @/@ admin1 %/% admin1a
ratio	num_enrolled_school_children %/% num_aged_school_children	NA %/% NA	admin1	admin1b	0.6461538	0.4939644	0.7983432	25	25	30.86420	30.86420	ratio @/@ num_enrolled_school_children %/% NA -/- num_aged_school_children %/% NA @/@ admin1 %/% admin1b
ratio	num_enrolled_school_children %/% num_aged_school_children	NA %/% NA	admin1	admin1c	0.7173913	0.5887553	0.8460273	35	35	41.66667	41.66667	ratio @/@ num_enrolled_school_children %/% NA -/- num_aged_school_children %/% NA @/@ admin1 %/% admin1c

Arguments of `create_analysis_ratio`

create_analysis_ratio has two arguments: numerator_NA_to_0 and filter_denominator_0 that by default are set to TRUE.

numerator_NA_to_0 will turn all NA of the numerator into 0’s, default TRUE.
filter_denominator_0 will remove all rows with 0’s in the denominator, default TRUE.

The following example show a dataset with the number of children (num_children), the number of children enrolled to a school (num_enrolled) and the number of children attending school on a regular basis (num_attending).

school_ex <- data.frame(
  hh = c("hh1", "hh2", "hh3", "hh4"),
  num_children = c(3, 0, 2, NA),
  num_enrolled = c(3, NA, 0, NA),
  num_attending = c(1, NA, NA, NA)
  )

me_design <- srvyr::as_survey(school_ex)

school_ex

hh	num_children	num_enrolled	num_attending
hh1	3	3	1
hh2	0	NA	NA
hh3	2	0	NA
hh4	NA	NA	NA

What is the ratio between children attending school and the number of children ?
How many households are included in the calculation?

Expand to get the answer

Default value will give a ratio of 0.2 as there are 1 child out of 5 attending school.

numerator: 1 child from hh1 and 0 from hh3.

denominator: 3 from hh1 and 2 from hh3. In the hh3, the num_attending is NA because there is a skip logic, there cannot be a child attending as none are enrolled.

By default, the function has the argument numerator_NA_to_0 set to TRUE to turn that NA into a 0.

n and n_total are 2 as 2 households were included in the calculation. hh2 was not included in the calculation of totals. The argument filter_denominator_0 set to TRUE removes that row.

create_analysis_ratio(me_design,
  analysis_var_numerator = "num_attending",
  analysis_var_denominator = "num_children") %>%
  select(analysis_type, analysis_var, stat, n, n_total, analysis_key)

analysis_type	analysis_var	stat	n	n_total	analysis_key
ratio	num_attending %/% num_children	0.2	2	2	ratio @/@ num_attending %/% NA -/- num_children %/% NA @/@ NA %/% NA

What will be the ratio if only numerator_NA_to_0 is set to FALSE ?
How many households are included in the calculation?

Expand to get the answer

Ratio will be 1/3, as hh3 with 2 children and NA for attending will be removed with the na.rm = T inside the survey_ratio calculation.

n and n_total is 1 as only 1 household was used.

create_analysis_ratio(me_design,
                      analysis_var_numerator = "num_attending",
                      analysis_var_denominator = "num_children",
                      numerator_NA_to_0 = FALSE) %>% 
  select(analysis_type, analysis_var, stat, n, n_total, analysis_key)

Warning: There were 2 warnings in `dplyr::summarise()`.
The first warning was:
ℹ In argument: `srvyr::survey_ratio(...)`.
Caused by warning in `qt()`:
! NaNs produced
ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.

analysis_type	analysis_var	stat	n	n_total	analysis_key
ratio	num_attending %/% num_children	0.3333333	1	1	ratio @/@ num_attending %/% NA -/- num_children %/% NA @/@ NA %/% NA

What will be the ratio if only filter_denominator_0 is set to FALSE ?
How many households are included in the calculation?

Expand to get the answer

ratio will be 0.2 as there are 1 child out of 5 attending school. The number of household counted, n and n_total, is equal to 3 instead 2. The household with 0 child is counted in the totals. (01 + 0 + 0) / (3 + 0 + 2)

create_analysis_ratio(me_design,
                      analysis_var_numerator = "num_attending",
                      analysis_var_denominator = "num_children",
                      filter_denominator_0 = FALSE)  %>% 
  select(analysis_type, analysis_var, stat, n, n_total, analysis_key)

analysis_type	analysis_var	stat	n	n_total	analysis_key
ratio	num_attending %/% num_children	0.2	3	3	ratio @/@ num_attending %/% NA -/- num_children %/% NA @/@ NA %/% NA

Analysis - Reviewing the analysis

review_*

review_* functions will review an object by comparing it to standards or another object and flags differences, e.g. reviewing the cleaning by comparing the raw dataset, the clean dataset and the cleaning log, analysis comparing it with another analysis.

`create_loa_from_results`

If the loa that was used was shared, it can be re-used. Otherwise, with the results table and the analysis key, the function create_loa_from_results will generate a loa that can be used to create the analysis for the review.

my_loa_for_review <- my_results_with_ratio$results_table %>% 
  create_loa_from_results()

my_loa_for_review

analysis_type	analysis_var	group_var	level	analysis_var_numerator	analysis_var_denominator	numerator_NA_to_0	filter_denominator_0
prop_select_one	admin1	NA	0.95	NA	NA	NA	NA
mean	income_v1_salaried_work	NA	0.95	NA	NA	NA	NA
median	income_v1_salaried_work	NA	0.95	NA	NA	NA	NA
mean	expenditure_debt	NA	0.95	NA	NA	NA	NA
median	expenditure_debt	NA	0.95	NA	NA	NA	NA
ratio	NA	NA	0.95	num_enrolled_school_children	num_aged_school_children	TRUE	TRUE
prop_select_one	wash_drinkingwatersource	NA	0.95	NA	NA	NA	NA
prop_select_multiple	edu_learning_conditions_reasons_v1	NA	0.95	NA	NA	NA	NA
mean	income_v1_salaried_work	admin1	0.95	NA	NA	NA	NA
median	income_v1_salaried_work	admin1	0.95	NA	NA	NA	NA
mean	expenditure_debt	admin1	0.95	NA	NA	NA	NA
median	expenditure_debt	admin1	0.95	NA	NA	NA	NA
ratio	NA	admin1	0.95	num_enrolled_school_children	num_aged_school_children	TRUE	TRUE
prop_select_one	wash_drinkingwatersource	admin1	0.95	NA	NA	NA	NA
prop_select_multiple	edu_learning_conditions_reasons_v1	admin1	0.95	NA	NA	NA	NA

Note

create_loa_from_results will not guess the arguments for numerator_NA_to_0 and filter_denominator_0, they will be set to TRUE by default.

The confidence level will also be set to .95 by default.

`review_analysis`

review_analysis will compare 2 results together and present the differences. It will not check how the analysis was created nor check for inconsistencies. That mean, to review an analysis, it is necessary to create one and compare them.

review_*

if the cleaning has been filled correctly
the cleaning has been done correctly
comparing indicators
comparing analysis
etc.

my_design_for_review <- srvyr::as_survey_design(my_data, weights = "weights", strata = "admin1")
analysis_for_review <- create_analysis(my_design_for_review, my_loa_for_review, sm_separator = "/")

binded_table <- my_results_with_ratio$results_table %>% 
  left_join(analysis_for_review$results_table, by = "analysis_key")

my_review <- review_analysis(binded_table)

typeof(my_review)

[1] "list"

names(my_review)

[1] "results_table" "review_table"

my_review$review_table %>%
  head()

analysis_key	stat	review_check	review_comment	analysis_type	analysis_var	group_var
prop_select_one @/@ admin1 %/% admin1a @/@ NA %/% NA	stat.x	TRUE	Same results	prop_select_one	admin1	NA
prop_select_one @/@ admin1 %/% admin1b @/@ NA %/% NA	stat.x	TRUE	Same results	prop_select_one	admin1	NA
prop_select_one @/@ admin1 %/% admin1c @/@ NA %/% NA	stat.x	TRUE	Same results	prop_select_one	admin1	NA
mean @/@ income_v1_salaried_work %/% NA @/@ NA %/% NA	stat.x	TRUE	Same results	mean	income_v1_salaried_work	NA
median @/@ income_v1_salaried_work %/% NA @/@ NA %/% NA	stat.x	TRUE	Same results	median	income_v1_salaried_work	NA
mean @/@ expenditure_debt %/% NA @/@ NA %/% NA	stat.x	TRUE	Same results	mean	expenditure_debt	NA

my_review$review_table %>%
  group_by(stat, review_check, review_comment) %>%
  tally()

stat	review_check	review_comment	n
stat.x	TRUE	Same results	147

Note

analysis_key are equivalent of an unique identifier. All analysis key should be unique.

jittered_results_table <- binded_table
set.seed(123)
jittered_results_table[sample(1:nrow(jittered_results_table), 5), "stat.x"] <- sample(unique(jittered_results_table$stat.x), 5, T)
set.seed(124)
jittered_results_table[sample(1:nrow(jittered_results_table), 5), "stat.y"] <- sample(unique(jittered_results_table$stat.y), 5, T)
set.seed(125)
jittered_results_table[sample(1:nrow(jittered_results_table), 5), "stat.x"] <- NA
set.seed(1236)
jittered_results_table[sample(1:nrow(jittered_results_table), 5), "stat.y"] <- NA

my_jittered_review <- review_analysis(jittered_results_table, 
                                      stat_columns_to_review = "stat.x",
                                      stat_columns_to_compare_with = "stat.y")
my_jittered_review$review_table %>%
  group_by(stat, review_check, review_comment) %>%
  tally()

stat	review_check	review_comment	n
stat.x	FALSE	Different results	10
stat.x	FALSE	Missing in stat.x	5
stat.x	FALSE	Missing in stat.y	5
stat.x	TRUE	Same results	127

my_jittered_review$results_table %>%
  filter(!review_check_stat.x) %>% 
  head(10)

analysis_type.x	analysis_var.x	analysis_var_value.x	group_var.x	group_var_value.x	stat.x	stat_low.x	stat_upp.x	n.x	n_total.x	n_w.x	n_w_total.x	analysis_key	analysis_type.y	analysis_var.y	analysis_var_value.y	group_var.y	group_var_value.y	stat.y	stat_low.y	stat_upp.y	n.y	n_total.y	n_w.y	n_w_total.y	review_check_stat.x	review_comment_stat.x
prop_select_one	wash_drinkingwatersource	borehole_tubewell	NA	NA	NA	-0.0009366	0.0839997	4	100	4.1531547	100.00000	prop_select_one @/@ wash_drinkingwatersource %/% borehole_tubewell @/@ NA %/% NA	prop_select_one	wash_drinkingwatersource	borehole_tubewell	NA	NA	0.0415315	-0.0009366	0.0839997	4	100	4.1531547	100.00000	FALSE	Missing in stat.x
prop_select_one	wash_drinkingwatersource	piped_into_compound	NA	NA	0.0657820	0.0199103	0.1354632	7	100	7.7686750	100.00000	prop_select_one @/@ wash_drinkingwatersource %/% piped_into_compound @/@ NA %/% NA	prop_select_one	wash_drinkingwatersource	piped_into_compound	NA	NA	0.0776867	0.0199103	0.1354632	7	100	7.7686750	100.00000	FALSE	Different results
prop_select_one	wash_drinkingwatersource	rain_water_collection	NA	NA	0.0291859	-0.0055007	0.0638725	3	100	2.9185868	100.00000	prop_select_one @/@ wash_drinkingwatersource %/% rain_water_collection @/@ NA %/% NA	prop_select_one	wash_drinkingwatersource	rain_water_collection	NA	NA	0.4285714	-0.0055007	0.0638725	3	100	2.9185868	100.00000	FALSE	Different results
prop_select_one	wash_drinkingwatersource	unprotected_spring	NA	NA	NA	-0.0055476	0.0656830	3	100	3.0067702	100.00000	prop_select_one @/@ wash_drinkingwatersource %/% unprotected_spring @/@ NA %/% NA	prop_select_one	wash_drinkingwatersource	unprotected_spring	NA	NA	0.0300677	-0.0055476	0.0656830	3	100	3.0067702	100.00000	FALSE	Missing in stat.x
prop_select_multiple	edu_learning_conditions_reasons_v1	lack_qualified_staff	NA	NA	NA	0.4723972	0.6786524	57	100	57.5524834	100.00000	prop_select_multiple @/@ edu_learning_conditions_reasons_v1 %/% lack_qualified_staff @/@ NA %/% NA	prop_select_multiple	edu_learning_conditions_reasons_v1	lack_qualified_staff	NA	NA	0.5755248	0.4723972	0.6786524	57	100	57.5524834	100.00000	FALSE	Missing in stat.x
mean	income_v1_salaried_work	NA	admin1	admin1b	0.1612903	19.0489416	20.5066140	27	27	33.3333333	33.33333	mean @/@ income_v1_salaried_work %/% NA @/@ admin1 %/% admin1b	mean	income_v1_salaried_work	NA	admin1	admin1b	19.7777778	19.0489416	20.5066140	27	27	33.3333333	33.33333	FALSE	Different results
mean	expenditure_debt	NA	admin1	admin1c	0.5229561	19.6006716	20.8755188	42	42	50.0000000	50.00000	mean @/@ expenditure_debt %/% NA @/@ admin1 %/% admin1c	mean	expenditure_debt	NA	admin1	admin1c	20.2380952	19.6006716	20.8755188	42	42	50.0000000	50.00000	FALSE	Different results
prop_select_one	wash_drinkingwatersource	water_kiosk	admin1	admin1a	NA	-0.0317653	0.0962814	1	31	0.5376344	16.66667	prop_select_one @/@ wash_drinkingwatersource %/% water_kiosk @/@ admin1 %/% admin1a	prop_select_one	wash_drinkingwatersource	water_kiosk	admin1	admin1a	0.0322581	-0.0317653	0.0962814	1	31	0.5376344	16.66667	FALSE	Missing in stat.x
prop_select_one	wash_drinkingwatersource	bottled_water	admin1	admin1b	0.0370370	-0.0364712	0.1105453	1	27	1.2345679	33.33333	prop_select_one @/@ wash_drinkingwatersource %/% bottled_water @/@ admin1 %/% admin1b	prop_select_one	wash_drinkingwatersource	bottled_water	admin1	admin1b	19.8709677	-0.0364712	0.1105453	1	27	1.2345679	33.33333	FALSE	Different results
prop_select_one	wash_drinkingwatersource	other	admin1	admin1c	0.0476190	-0.0183900	0.1136281	2	42	2.3809524	50.00000	prop_select_one @/@ wash_drinkingwatersource %/% other @/@ admin1 %/% admin1c	prop_select_one	wash_drinkingwatersource	other	admin1	admin1c	20.0000000	-0.0183900	0.1136281	2	42	2.3809524	50.00000	FALSE	Different results

Exercises

Create the analysis for the following indicators:

% of households having/had soap at home
% of households by type of primary source of drinking water
% of households by self-reported barriers to accessing health care
Average household income over the 30 days prior to data collection (total)
Median household income over the 30 days prior to data collection (total)
Average household expenditures in the 6 months prior to data collection (health)
Median household expenditures in the 6 months prior to data collection (health)
Ratio household expenditures on health in the 6 months prior to data collection and the household income over the 30 days prior to data collection.
% of households per number of days when the household had to restrict consumption by adults in order for small children to eat to cope with a lack of food or money to buy it.

The analysis should be at admin1 level (the strata).

name	label::english	type
income_v2_total	Can you estimate your household’s total income (in local currency) over the last 30 days from all sources? Please only report income received in the form of money, not items or services.	integer
expenditure_health	3. Health-related expenditures (healthcare, medicine, etc.)	integer
rCSIMealAdult	During the last 7 days, were there days (and, if so, how many) when your household had to restrict consumption by adults in order for small children to eat to cope with a lack of food or money to buy it?	integer
wash_drinkingwatersource	What is the main source of drinking water for members of your household?	select_one wash_drinkingwatersource
wash_soap	Do you have soap or detergent in your household for washing hands? [if not remote] Can you show it to me?	select_one wash_soap
health_barriers	What are your barriers to access health care?	select_multiple health_barriers

exercise_data <- analysistools::analysistools_MSNA_template_data

exercise_sampling_frame <- data.frame(
  strata = c("admin1a", "admin1b", "admin1c"),
  population = c(100000, 200000, 300000)
)

template_loa <- readxl::read_excel("inputs/08 - exercise - template loa.xlsx")

Expand to get a hint

rCSIMealAdult should be analysed as a categorical variable not a numerical variable to get the proportion per day.

Expand to get the answer

exercise_data <- exercise_data %>%
  add_weights(
    exercise_sampling_frame,
    "admin1", "strata", "population"
  )

exercise_design <- srvyr::as_survey_design(exercise_data, weights = "weights", strata = "admin1")

exercise_loa <- readxl::read_excel("inputs/09 - correction - loa.xlsx")

exercise_results <- create_analysis(exercise_design, loa = exercise_loa, sm_separator = "/")

Review this analysis

There is no weights. The strata are admin1

analysis_to_review <- readxl::read_excel("inputs/10 - exercise - analysis_to_review.xlsx")
dataset_to_review <- readxl::read_excel("inputs/10 - exercise - analysis_to_review.xlsx", sheet = "dataset")

Expand to get the answer

loa_for_review <- analysis_to_review %>% 
  create_loa_from_results()

review_design <- srvyr::as_survey_design(dataset_to_review, strata = "admin1")
my_analysis_exercise <- create_analysis(review_design, loa = loa_for_review, sm_separator = "/")

Joining with `by = join_by(admin1, respondent_gender)`
Joining with `by = join_by(admin1, ind_gender)`
Joining with `by = join_by(admin1, caregiver_available)`
Joining with `by = join_by(admin1, difficulty_self_care)`
■■■■■■■■ 24% | ETA: 7s
Joining with `by = join_by(admin1, edu_modality_v2)`
Joining with `by = join_by(admin1, liv_emerg_csi_3)`
Joining with `by = join_by(admin1, fs_hhs_nofood_yn)`
Joining with `by = join_by(admin1, wash_handwashingfacility_observed_water)`
Joining with `by = join_by(admin1, wash_handwashingfacility_observed_soap)`
■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 94% | ETA: 0s
Joining with `by = join_by(admin1, hoh_age)`

my_results_table_shorter <- my_analysis_exercise$results_table %>% 
  select(analysis_key, stat)

binded_results_table <- analysis_to_review %>% 
  full_join(my_results_table_shorter, by = "analysis_key")

exercise_review <- review_analysis(binded_results_table,
                                   stat_columns_to_review = "stat.x",
                                   stat_columns_to_compare_with = "stat.y", 
                                   analysis_key_column = "analysis_key")

exercise_review$review_table %>% 
  group_by(review_check,review_comment) %>% 
  tally()

review_check	review_comment	n
TRUE	Same results	134

How you would review an analysis that does not have an analysis key? (discussion)

Expand to get the answer

If the analysis is in long format, add the analysis key.
If the analysis is in a wide format, change to long format then add the analysis key.

Outputs

library(presentresults)

my_results_table <- my_results$results_table

The framework is built around 4 steps: cleaning, composition, analysis, outputs

Cleaning: Any manipulation to go from the raw data to the clean data
Composition: Any manipulation before the analysis e.g. adding indicators, adding information from loop, main dataset, or any other dataset (e.g. previous round), aok aggregation, etc.
Analysis: Any manipulation regarding only the analysis.
Outputs: Any manipulation to format the outputs. Outputs are created from the results table, from the stat + analysis key

The following section will present some introduction about the outputs.

There are currently two types of table:

one that have the variables in the rows and the disagregation in the columns,
one that have the disagregation in the rows and the variables in the columns.

There are two steps to turn a results table:

Turn the long results table to a large results table.
Format and export it to Excel.

create_*

create_* functions will create, transform something, e.g. creating a cleaning log with the checks to be filled, create analysis results table, create an output.

Outputs from create_* functions outputs can be in different shape, format, etc.

create_* function is catch-all.

create_*_group_x_variable

my_results_table %>% 
  create_table_group_x_variable() %>% 
  create_xlsx_group_x_variable(file_path = "outputs/04 - example - group_x_variable.xlsx", overwrite = T)

create_*_variable_x_group

my_results_table %>% 
  create_table_variable_x_group() %>%
  create_xlsx_variable_x_group(file_path = "outputs/05 - example - variable_x_group.xlsx", overwrite = T)

Exercise

Create an excel table with the strata in the rows and the variables in the columns.

exercise_outputs <- readxl::read_excel("inputs/10 - exercise - analysis_to_review.xlsx")

Expand to get the answer

exercise_outputs %>% 
  create_table_group_x_variable() %>% 
  create_xlsx_group_x_variable(file_path = "outputs/06 - correction - group_x_variable_table.xlsx", overwrite = T)

Analysis - extended analysis

create_analysis and list of analysis (loa)

create_analysis_ratio

Arguments of create_analysis_ratio

Analysis - Reviewing the analysis

create_loa_from_results

review_analysis

Exercises

Outputs

create_*_group_x_variable

create_*_variable_x_group

Exercise

`create_analysis` and list of analysis (loa)

`create_analysis_ratio`

Arguments of `create_analysis_ratio`

`create_loa_from_results`

`review_analysis`